如何扩容Bazel缓存服务器？企业多用户场景下的优化方案问询

Hey there, let's tackle this Bazel cache scaling issue you're facing. Your initial thought of dedicating more resources to the cache server is a solid starting point, but there are a few structured approaches you can take to handle growing user load smoothly. Here are the most practical solutions:

First, let's double down on your initial plan—this is the quickest win if your current server has headroom:

• Upgrade hardware: Swap in higher-core CPUs (Nginx benefits from more worker processes), increase RAM (to reduce disk I/O by keeping frequently accessed cache entries in memory), and upgrade your SSD to a higher IOPS model (or even a RAID 0/10 array of NVMe drives for parallel I/O).
• Tune Nginx configurations: Optimize Nginx to handle more concurrent connections:

  # Set worker processes to match CPU cores
  worker_processes auto;
  # Increase max connections per worker
  worker_connections 10240;
  # Enable efficient event handling
  events {
      use epoll;
      multi_accept on;
  }
  

  Also, enable gzip compression for cache metadata and ensure you're using appropriate cache headers to reduce redundant requests.

When vertical scaling hits its limit, horizontal scaling is the way to go. Bazel's content-addressable cache makes this straightforward because cache entries are keyed by their content hash—no need for cross-node synchronization:

• Set up a load balancer: Use a primary Nginx instance as a reverse proxy/load balancer to distribute traffic across multiple cache backend nodes (each running Nginx + SSD). Here's a sample upstream config:

  upstream bazel_cache_backends {
      server cache-node-1.internal:80 weight=3; # Heavier weight for more powerful nodes
      server cache-node-2.internal:80;
      server cache-node-3.internal:80 backup; # Fallback node
      # Add health checks to avoid routing to down nodes
      keepalive 32;
  }
  
  server {
      listen 80;
      server_name your-bazel-cache.com;
  
      location / {
          proxy_pass http://bazel_cache_backends;
          proxy_set_header Host $host;
          proxy_cache_bypass $http_upgrade;
          # Pass through cache-related headers to backends
          proxy_set_header X-Forwarded-For $remote_addr;
      }
  }
  

• Add nodes on demand: Whenever load increases, spin up a new cache node (with identical Nginx/SSD setup) and add it to the upstream pool. Clients will automatically distribute requests, and misses on one node will be populated from the build source, then cached locally.

If you want to avoid duplicate cache entries across nodes, use a high-performance shared storage layer (like NVMe over Fabrics or a fast NAS) paired with multiple Nginx frontends:

• Mount the shared storage to all Nginx servers (ensure the storage supports concurrent read/write with low latency).
• Configure each Nginx frontend to serve cache content directly from the shared storage. This way, all nodes access the same cache pool, eliminating redundant storage usage.
• Note: This approach relies heavily on the shared storage's performance—make sure it can handle the combined I/O load from all frontends.

Before jumping into full-scale scaling, these tweaks can lighten the load on your existing setup:

• Promote local Bazel caches: Guide your users to configure Bazel's local disk cache with --disk_cache=/path/to/local/cache or enable the built-in local cache. This reduces remote cache hits for frequently built targets.
• Clean up stale cache: Schedule regular cleanup of old, unused cache entries to free up SSD space and maintain performance. For example, run this cron job weekly:

  find /path/to/bazel/cache -type f -atime +30 -delete
  

• Enable Nginx proxy caching: If you haven't already, add a proxy cache layer in Nginx to cache frequently accessed entries in memory, reducing direct SSD I/O. Example config:

  proxy_cache_path /var/cache/nginx/bazel levels=1:2 keys_zone=bazel_cache:10m max_size=10g inactive=7d use_temp_path=off;
  
  server {
      # ... other configs
      location / {
          proxy_cache bazel_cache;
          proxy_pass http://your-storage-backend;
          proxy_cache_valid 200 7d;
          # Serve stale cache if backend is down
          proxy_cache_use_stale error timeout updating;
      }
  }
  

Final Recommendation #

Start with vertical scaling and the load-reducing optimizations—they're low-effort and often sufficient for moderate user growth. If you still see bottlenecks, move to horizontal clustering (it's the most scalable option for Bazel's cache model). Centralized shared storage works well if you want to avoid duplicate data, but ensure your storage layer can keep up with I/O demands.

内容的提问来源于stack exchange，提问作者Zhuo Zhang